"""
Creates a MobileNetV3 Model as defined in:
Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, Hartwig Adam. (2019).
Searching for MobileNetV3
arXiv preprint arXiv:1905.02244.
"""

import torch.nn as nn
import math


# def make_model(args):
#     args = args[0]
#     if args.data_train.find('CIFAR') >= 0:
#         num_classes = int(args.data_train[5:])
#     elif args.data_train.find('Tiny') >= 0:
#         num_classes = 200
#     else:
#         num_classes = 1000
#     width_mult = args.width_mult
#     if args.template.find('large') >= 0:
#         return mobilenetv3_large(num_classes=num_classes, width_mult=width_mult)
#     else:
#         return mobilenetv3_small(num_classes=num_classes, width_mult=width_mult)
#
#
#
# __all__ = ['mobilenetv3_large', 'mobilenetv3_small']

def make_model(args):
    return  MobileNetV3(args[0])


def _make_divisible(v, divisor, min_value=None):
    """
    This function is taken from the original tf repo.
    It ensures that all layers have a channel number that is divisible by 8
    It can be seen here:
    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
    :param v:
    :param divisor:
    :param min_value:
    :return:
    """
    if min_value is None:
        min_value = divisor
    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
    # Make sure that round down does not go down by more than 10%.
    if new_v < 0.9 * v:
        new_v += divisor
    return new_v


class h_sigmoid(nn.Module):
    def __init__(self, inplace=True):
        super(h_sigmoid, self).__init__()
        self.relu = nn.ReLU6(inplace=inplace)

    def forward(self, x):
        return self.relu(x + 3) / 6


class h_swish(nn.Module):
    def __init__(self, inplace=True):
        super(h_swish, self).__init__()
        self.sigmoid = h_sigmoid(inplace=inplace)

    def forward(self, x):
        return x * self.sigmoid(x)


class SELayer(nn.Module):
    def __init__(self, channel, reduction=4):
        super(SELayer, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
                nn.Linear(channel, channel // reduction),
                nn.ReLU(inplace=True),
                nn.Linear(channel // reduction, channel),
                h_sigmoid()
        )

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y = self.fc(y).view(b, c, 1, 1)
        return x * y


def conv_3x3_bn(inp, oup, stride):
    return nn.Sequential(
        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
        nn.BatchNorm2d(oup),
        h_swish()
    )


def conv_1x1_bn(inp, oup):
    return nn.Sequential(
        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
        nn.BatchNorm2d(oup),
        h_swish()
    )


class InvertedResidual(nn.Module):
    def __init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se, use_hs):
        super(InvertedResidual, self).__init__()
        assert stride in [1, 2]

        self.use_res_connect = stride == 1 and inp == oup

        layers = []
        # pw
        if inp != hidden_dim:
            layers.extend([
                nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
                nn.BatchNorm2d(hidden_dim),
                h_swish() if use_hs else nn.ReLU(inplace=True)])
        # dw
        layers.extend([
            nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, kernel_size // 2, groups=hidden_dim, bias=False),
            nn.BatchNorm2d(hidden_dim),
            h_swish() if use_hs else nn.ReLU(inplace=True)
        ])
        if use_se:
            layers.append(SELayer(hidden_dim))
        # pw-linear
        layers.extend([
            nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
            nn.BatchNorm2d(oup)
        ])
        self.conv = nn.Sequential(*layers)

    def forward(self, x):
        if self.use_res_connect:
            return x + self.conv(x)
        else:
            return self.conv(x)


# MobileNetV3-Large model configuration
cfgs_large = [
    # k,  t,  c, SE, NL,s
    [3,  16,  16, 0, 0, 1],
    [3,  64,  24, 0, 0, 2],
    [3,  72,  24, 0, 0, 1],
    [5,  72,  40, 1, 0, 2],
    [5, 120,  40, 1, 0, 1],
    [5, 120,  40, 1, 0, 1],
    [3, 240,  80, 0, 1, 2],
    [3, 200,  80, 0, 1, 1],
    [3, 184,  80, 0, 1, 1],
    [3, 184,  80, 0, 1, 1],
    [3, 480, 112, 1, 1, 1],
    [3, 672, 112, 1, 1, 1],
    [5, 672, 160, 1, 1, 1],
    [5, 672, 160, 1, 1, 2],
    [5, 960, 160, 1, 1, 1]
]


# MobileNetV3-Small model configuration
cfgs_small = [
    # k,  t,  c, SE, NL,s
    [3,  16,  16, 1, 0, 2],
    [3,  72,  24, 0, 0, 2],
    [3,  88,  24, 0, 0, 1],
    [5,  96,  40, 1, 1, 2],
    [5, 240,  40, 1, 1, 1],
    [5, 240,  40, 1, 1, 1],
    [5, 120,  48, 1, 1, 1],
    [5, 144,  48, 1, 1, 1],
    [5, 288,  96, 1, 1, 2],
    [5, 576,  96, 1, 1, 1],
    [5, 576,  96, 1, 1, 1],
]


class MobileNetV3(nn.Module):
    def __init__(self, args):
        super(MobileNetV3, self).__init__()
        self.mode = args.mode
        mode = args.mode
        width_mult = args.width_mult

        if args.data_train.find('CIFAR') >= 0:
            num_classes = int(args.data_train[5:])
        elif args.data_train.find('Tiny') >= 0:
            num_classes = 200
        else:
            num_classes = 1000

        # setting of inverted residual blocks
        assert mode in ['large', 'small']
        if mode == 'large':
            self.cfgs = cfgs_large
        else:
            self.cfgs = cfgs_small

        # building first layer
        input_channel = _make_divisible(16 * width_mult, 8)
        stride = 2 if args.data_train == 'ImageNet' else 1
        layers = [conv_3x3_bn(3, input_channel, stride)]

        # building inverted residual blocks
        block = InvertedResidual
        for k, exp_size, c, use_se, use_hs, s in self.cfgs:
            exp_size = _make_divisible(exp_size * width_mult, 8)
            output_channel = _make_divisible(c * width_mult, 8)
            layers.append(block(input_channel, exp_size, output_channel, k, s, use_se, use_hs))
            input_channel = output_channel
        self.features = nn.Sequential(*layers)

        # building last several layers
        conv = [conv_1x1_bn(input_channel, exp_size)]
        if mode == 'small':
            conv.append(SELayer(exp_size))
        self.conv = nn.Sequential(*conv)

        # classifier
        output_channel = _make_divisible(1280 * width_mult, 8) if width_mult > 1.0 else 1280
        # self.classifier = nn.Sequential(
        #     nn.Conv2d(exp_size, output_channel, 1),
        #     h_swish(),
        #     nn.Conv2d(output_channel, num_classes, 1)
        # )

        self.classifier = nn.Sequential(
            nn.Linear(exp_size, output_channel),
            h_swish(),
            nn.Linear(output_channel, num_classes)
        )

        self._initialize_weights()

    def forward(self, x):
        x = self.features(x)
        x = self.conv(x)
        x = x.mean([2, 3])
        x = self.classifier(x)
        # x = self.classifier(x).squeeze()
        return x

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
                if m.bias is not None:
                    m.bias.data.zero_()
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                n = m.weight.size(1)
                m.weight.data.normal_(0, 0.01)
                m.bias.data.zero_()
